Our analysis took place in roughly 3 parts. The first was initial analysis looking at the basic features of the flood dataset. It sought to get a picture of the how the data was distibuted, what was typical, and to generate hypotheses about potential explanations for the variation in the dimentsions. The second sought to examine the connection between geopotential height and flooding. Lastly, we examined the impacts of flooding and what explained the (sometimes very large) differences in the damage done by these events.
We started our analysis by trying to get a basic sense of the properties of the floods recorded in the dataset. This meant looking for things like the spatial and temporal distribution of flood, the distribution of flood size, duration, and area affected, and distributions of . We wanted to see what variation there was between floods, and if we could get any indicators of what was explaining that variation.
Many of the above plots mostly follow a ‘power law’ distribution, with the bulk of the mass residing in the first 20 percent of the distribution and with a thin but long tail of extreme outlying events. Subsetting and faceting the distributions by type, cause, severity didn’t yield significantly different distributions.
This plot shows the spatial distribution of floods by cause. Not surprisingly, the plot indicates that the geography has a significant effect on the type of flooding experinced in different parts of the world. Floods due to Monsoon, for example happen primarily in the Indian Ocean and Southeast Asia; floods cause by snow melt happen at more northerly mountainous regions; floods caused by hurricaines and tropical storms happen mostly along the coast in warmer, more tropical latitudes. Floods caused by heavy rainfall were present all over the world (except in expected locations like desserts). Further (and also not surprisingly), we noticed that floods occur most frequently near rivers and coastlines.
The density maps allow for somewhat easier interpretation of where the bulk of floods occur. As mentioned previously and as vividly indicated by the monsoon density map, densities often center near rivers and coastlines, such as the Ganges River Delta.
Another to cut this data is to look at the changing patterns through time. Here we animate the world map to show floods in each year.
While there is a lot of information here, it is more instructive to pick a particular area, and compare flooding to geopotential height to visually look for any correlations that may exist. We look at India in the summer of 2007 due to the large amount of flooding.